There is something quite alien when imagining a swarm type intelligence. A bunch of little creatures who act as if directed by one being. Swarming is the spontaneous organized motion of a large number of individuals. It is observed at all scales, from bacterial colonies, slime molds and groups of insects to shoals of fish, flocks of birds and animal herds. Now physicists Maksym Romenskyy and Vladimir Lobaskin from university College Dublin, Ireland, have uncovered new collective properties of swarm dynamics in a study just published in EPJ B. Ultimately, this could be used to control swarms of animals, robots, or human crowds by applying signals capable of emulating the underlying interaction of individuals within the swarm, which could lead to predicted motion patterns elucidated through modelling.

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Swarm behavior, or swarming, is a collective behavior exhibited by animals of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en mass or migrating in some direction. As a term, swarming is applied particularly to insects, but can also be applied to any other animal that exhibits swarm behavior. The term flocking is usually used to refer specifically to swarm behavior in birds, herding to refer to swarm behavior in quadrupeds, shoaling or schooling to refer to swarm behavior in fish. Phytoplankton also gather in huge swarms called blooms, although these organisms are algae and are not self-propelled the way animals are. By extension, the term swarm is applied also to inanimate entities which exhibit parallel behaviors, as in a robot swarm, an earthquake swarm, or a swarm of stars.

From a more abstract point of view, swarm behavior is the collective motion of a large number of self-propelled entities. From the perspective of the mathematical modeller, it is an emergent behavior arising from simple rules that are followed by individuals and does not involve any central coordination.

The new study authors were inspired by condensed matter models, used for example in the study of magnetism, which were subsequently adapted to be biologically relevant to animal swarms. In their model, in addition to the ability to align with its neighbors, each model animal is endowed with two new features: one for collision avoidance and another preventing direction change at every step to ensure persistence of motion. The team performed computer simulations of up to 100,000 self-propelled particles, each mimicking an individual animal and moving at a constant speed on a plane surface.

They found that when the swarm becomes overcrowded, the globally ordered motion breaks down. At high density and when the nearest neighbors are within one step of each other, each animal can no longer decide on the safe direction of motion. Instead, it is busy correcting its motion to avoid collisions.

They also described, for the first time, a power law that quantifies the average degree of alignment in the direction of motion for animals within the swarm. The law describes how the alignment decays from the center of the swarm, where animals can best judge the swarm motion due to their maximum number of neighbors, to the periphery.